Real-Time Radio Modulation Classification With An LSTM Auto-Encoder

Abstract

Identifying modulation type of a received radio signal is a challenging problem encountered in many applications including radio interference mitigation and spectrum allocation. This problem is rendered challenging by the existence of a large number of modulation schemes and numerous sources of interference. Existing methods for monitoring spectrum readily collect large amounts of radio signals. However, existing state-of-the-art approaches to modulation classification struggle to reach desired levels of accuracy with computational efficiency practically feasible for implementation on low-cost computational platforms. To this end, we propose a learning framework based on an LSTM denoising autoencoder designed to extract robust and stable features from the noisy received signals, and detect the underlying modulation scheme. The method uses a compact architecture that may be implemented on low-cost computational devices while achieving or exceeding state-of-the-art classification accuracy. Experimental results on realistic synthetic and over-the-air radio data show that the proposed framework reliably and efficiently classifies radio signals, and often significantly outperform state-of-the-art approaches.